Delay-aware fog computing is a key research direction that focuses on minimizing latency in service delivery by processing data and executing tasks closer to end devices within fog infrastructures. Research papers in this domain propose delay-aware task scheduling, resource allocation, and service placement strategies to meet stringent Quality of Service (QoS) requirements for real-time and latency-sensitive applications. Studies explore dynamic offloading mechanisms, predictive models, and optimization algorithms that adapt to varying workloads, user mobility, and heterogeneous fog resources to reduce response times. Recent works integrate machine learning, deep reinforcement learning, and heuristic/metaheuristic approaches to design intelligent frameworks for delay minimization. Multi-objective optimization that balances delay with energy consumption, cost, and reliability is also emphasized. Security- and privacy-preserving delay-aware frameworks are gaining attention to ensure trust while maintaining low latency. Applications include smart healthcare, autonomous vehicles, augmented/virtual reality, industrial IoT, and smart cities, where near real-time responsiveness is critical. Overall, research in delay-aware fog computing enables efficient, adaptive, and scalable systems that support ultra-low-latency services in next-generation distributed environments.